Why this work matters

• EMS and trauma systems critically depend on timely, accurate performance data
  
• Hospitals and EMS services often lack analytic infrastructure or staff
  
• Most U.S. jurisdictions require reporting to a centralized trauma registry and EMS registry
• Each hospital and EMS service have access to their own raw data via their Electronic Health Record (EHR)
• National Trauma Data Bank (NTDB) and National EMS Information System (NEMSIS) define strong data standards and naming conventions for data elements
• Level I, II, and III hospitals report to NTDB, while all EMS service data will be reported to NEMSIS
• NEMSIS provides national/state/region level performance data, but local implementations for services is hard (many volunteer, low uptake of analytical resources)
• American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) provides some performance data, but not all hospitals report to TQIP (mostly Level I, II, and III programs)
• EHRs used by hospitals and EMS providers typically provide some analytical resources and reporting software, but this can be inflexible and difficult to use
• traumar and nemsqar bring modern R-based QI tools to frontline users

Overview: National Context

• NTDB: trauma registry data, hospital-based
  
• NEMSIS: national EMS data, prehospital events
  
• NEMSQA: National EMS Quality Alliance
• Non-profit, expert EMS stakeholders, consistent measure development process
• Founded in 2019. In the last 3-4 years, has published 21 measures of EMS service quality for public use
• SEQIC: System Evaluation and Quality Improvement Committee
• Iowa-based group of trauma stakeholders led by Iowa HHS
• Over the past two decades, the group has designed multiple QI measures for the Iowa trauma system
• Consistent element names, standardized formats
  
• Ideal foundation for reproducible QI pipelines

Introducing traumar

• Implements metrics driven the by academic literature
• Risk-adjusted mortality metrics
• W, M, and Z scores (Based on the Major Trauma Outcomes Study)
• Relative mortality metric (RMM) from Napoli et a. (2017)
• Built to calculate SEQIC indicators
• Friendly to NTDB data formats and others
  
• Calculates measures for Level I, II, III, and IV trauma centers
• Can perform analyses at multiple levels (i.e. hospital, regional, state)

traumar: Example Functions

traumar::seqic_airway_by_year(data)
traumar::trauma_performance(data)
traumar::rmm(data)
traumar::rm_bin_summary(data)

nemsqar: EMS QI with NEMSIS

• Built on NEMSQA performance measures
• Automatically detects and validates key elements
• Scales to full state-level EMS datasets

nemsqar: Example Functions

nemsqar::adult_population(data)
nemsqar::trauma_population(data)
nemsqar::respiratory_01(data)

• *_population() functions define eligible cases
• Wrapper functions implement full NEMSQA measures

Real-world Use

• traumar: Used to report risk-adjusted benchmarking and SEQIC indicators for all 120 trauma centers in Iowa

• nemsqar: Supporting the first statewide reporting on all NEMSQA measures in Iowa (only a few other states have done this with all 21 measures)

• Reproducible, interpretable, fast

Needs

• You!
• I welcome other developers with expertise in trauma, EMS, epidemiology, and data science to become a collaborator
• I am currently the only developer on the traumar project and there is so much room to grow
• nemsqar has one other developer
• Samuel Kordik: Quality Manager, Dallas Fire, Dallas, TX
• and a contributor
• Alyssa Green, Data Scientist, ESO, Kansas
• Please reach out. Open source is all about collaboration and we are very open to more contributors.

Thank you!

Questions?

Nicolas Foss, Ed.D., MS
Epidemiologist
Bureau of Emergency Medical and Trauma Services
Bureau of Health Statistics
Division of Public Health
Iowa Department of Health and Human Services
e: nicolas.foss at hhs.iowa.gov
c: 515-985-9627